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It-PassportsのIT専門家たちは彼らの豊富な知識と経験を活かして最新の短期で成果を取るDP-800トレーニング方法を研究しました。このトレーニング方法は受験生の皆さんに短い時間で予期の成果を取らせます。特に仕事しながら勉強している受験生たちにとって不可欠なツールです。DP-800トレーニング資料を選んだら、あなたは自分の夢を実現できます。
質問 # 19
You have an Azure SQL database that contains a table named dbo.Products. dbo.Products contains three columns named Embedding, Category, and Price. The Embedding column is defined as VECTOR(1536).
You use AI_GENERATE_EMBEDDINGS and VECTOR_SEARCH to support semantic search and apply additional filters on two columns named Category and Price.
You plan to change the embedding model from text-embedding-ada-002 to text-embedding-3- small. Existing rows already contain embeddings in the Embedding column.
You need to implement the model change. Applications must be able to use VECTOR_SEARCH without runtime errors.
What should you do first?
正解：A
解説：
To ensure your applications can transition models without runtime errors while using VECTOR_SEARCH, you must first define a Vector Index that explicitly identifies the dimensions and distance metric.
Since you are moving from text-embedding-ada-002 to text-embedding-3-small, both models default to 1536 dimensions, which matches your existing column definition. To create the index as the first step, use the following SQL:
CREATE VECTOR INDEX idx_embedding ON YourTableName (Embedding)
WITH ( DISTANCE_METRIC = 'COSINE' );
Use code with caution.
Why this works:
Schema Consistency: Because both models use 1536 dimensions, you don't need to alter the VECTOR(1536) column type immediately.
Search Stability: Creating the index allows the engine to optimize the VECTOR_SEARCH function. As long as the incoming query vector (generated by the app) matches the dimensions of the stored vectors, the search will execute without a runtime dimension mismatch error.
Reference:
https://docs.couchbase.com/cloud/n1ql/n1ql-language-reference/vectorfun.html
質問 # 20
What is the benefit of using embeddings over keyword search?
正解：D
解説：
Embeddings capture semantic meaning, enabling context-based retrieval.
質問 # 21
You are creating a table that will store customer profiles.
You have the following Transact-SQL code.
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection Is worth one point.
正解：
解説：
Explanation:
* The schema meets the security requirements for PII data. # Yes
* Administrators of the Azure SQL server can see all the rows in dbo.CustomerProfiles when they use an application. # No
* The masking rules will apply even when row-level security (RLS) filters out rows. # No The first statement is Yes because the design combines two relevant SQL security controls for personally identifiable information: Dynamic Data Masking (DDM) on sensitive columns such as FullName, EmailAddress, and PhoneNumber, and Row-Level Security (RLS) to restrict which rows a user can access based on RegionCode. Microsoft documents that DDM limits sensitive data exposure for nonprivileged users
, while RLS restricts row access according to the user executing the query. Together, these are valid and appropriate controls for protecting PII in Azure SQL Database.
The second statement is No . Administrative users can view unmasked data because administrative roles effectively have CONTROL, which includes UNMASK. However, that does not mean they automatically see all rows through the application query path defined by the RLS policy. The security policy filters rows based on SUSER_SNAME() and matching RegionCode, so row visibility is governed by the predicate unless the policy is altered or bypassed administratively. DDM and RLS solve different problems: DDM affects how returned values are shown, while RLS affects which rows are returned at all.
The third statement is No because masking only applies to data that is actually returned in the query result set.
Microsoft describes DDM as hiding sensitive data in the result set of a query . If RLS filters a row out, that row is not returned, so there is nothing left for masking to act on. In other words, RLS eliminates inaccessible rows first from the user's perspective, and DDM masks sensitive column values only on rows the user is allowed to see.
質問 # 22
You have an Azure SQL database that contains a table named Tickets_Embeddings. Tickets_Embeddings contains a VECTOR(1536) column.
Embeddings are generated by using text-embedding-ada-002 and the /openai/v1/embeddings endpoint.
After a review of the current environment, the following changes are requested:
The AI architect has recommended switching to text-embedding-3-small.
The security team requires authentication by using Microsoft Entra managed identities only. Storing API keys in an application is prohibited.
Which two actions should you include in the solution? Each correct answer presents a part of the solution.
NOTE: Each correct selection is worth one point.
正解：B、C
質問 # 23
Drag and Drop Question
You have an Azure SQL database that contains a table named Sales.Orders. Sales.Orders contains the following columns.
Reporting queries frequently repeat logic to calculate the number of days since an order was placed.
You need to create a scalar user-defined function (UDF) that returns the number of days between an input value of @OrderDate and the current date and time.
How should you complete the Transact-SQL code? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
正解：
解説：
質問 # 24
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